Relationship between tumor microenvironment (TME)-based histomic TGFβ signature (TGFBs), stromal fibroblast recruitment, and exclusion of immune cells as immunotherapy resistance mechanisms.
Authors: Gahee Park, Jongchan Park, Jeanne Shen, Yoon-La Choi, Taebum Lee, Hyojin Kim, Young Kwang Chae, Se-Hoon Lee, Sehhoon Park, Jin-haeng Chung, Chiyoon Oum, Minuk Ma, Melody SeungHui Seo, Chan-Young Ock
Journal: Journal of Clinical Oncology (2023)

2585 Background: TGF-beta activates fibroblasts within the TME and may be associated with poor response to immunotherapy.However, objective and reliable assessment of the TGFBs by gene expression profiling (GEP) is challenging. Using H&amp;E whole-slide images (WSI) from The Cancer Genome Atlas (TCGA), we developed an artificial intelligence (AI) model to predict a normalized TGFBs, applying it to real-world datasets (RWDs) of advanced solid tumor patients treated with immunotherapy. Methods: The TGFBs was defined using the mean GEP values of the 'Hallmark TGF-beta signaling' gene set. H&amp;E WSIs of 23 carcinoma types from TCGA (n=6,945) were used to discover the association of the TGFBs with various cell types, including fibroblasts and tumor-infiltrating lymphocytes (TILs) detected by an AI model, Lunit SCOPE IO (AI-1). Another custom AI model (AI-2) to predict TGFBs was developed by integrating self-supervised and supervised features and other semantic content extracted by AI-1 with multilayered perceptron classifiers. TGFBs prediction scores from the AI-2 were applied to 5 different RWDs containing 1,792 immunotherapy-treated patients (&gt; 16 primary tumor types). The immune-excluded phenotype (IEP) was defined by high stromal TIL and low intratumoral TIL density. Results: In the TCGA dataset, TGFBs and fibroblast density detected by AI-1 were closely correlated (R=0.19, p&lt;0.001) compared to other cell types, with both highest in pancreatic adenocarcinoma (PAC)(median [interquartile range, IQR] 0.73 [0.66-0.80], 1306 [985-1588]/mm 2 , respectively) and CMS4 colorectal cancer (0.61 [0.54-0.67], 868 [582-1128]/mm 2 ) compared to other cancers (0.57 [0.47-0.66], 577 [295-890]/mm 2 ). The AI-2 predicted TGFBs with area-under-the-curve of 0.781 on cross-validation within the TCGA dataset. The TGFBs of immunotherapy-treated RWDs were inferred from their corresponding H&amp;E WSI, with scores comparable to those from the TCGA (0.42 [0.16-0.60] vs 0.58 [0.48-0.67]), with PAC having the highest TGFBs (0.65 [0.45-0.77]) in the RWD as well. Interestingly, TGFBs inferred scores and fibroblast density were significantly correlated (R=0.7, p&lt;0.001) in the RWD and also significantly correlated with the IEP (0.52 [0.30-0.66]) compared to other immune phenotypes (0.42 [0.16-0.60], p&lt;0.001). In the RWD, patients with inferred TGFBs in the upper 75% quartile had a poor clinical response to immunotherapy (objective response rate: 15.1% vs 20.7%, median progression-free survival 3.0 m vs 4.0 m, hazard ratio 1.21, 95% confidence interval 1.07-1.36, p=0.002). Conclusions: TGFBs is closely correlated with fibroblast density within the TME and may confer resistance to immunotherapy. Unique morphologic features of the TGFBs can be captured by AI, enabling scalable application to various datasets for clinical and translational research.